Clayton Barrows

The classic pump scheduling or optimal water flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixed-integer nonlinear program (MINLP). It is complicated by nonlinear equality constraints that model network physics, discrete variables that model operational controls, and intertemporal constraints that model changes to storage devices. To address the computational challenges of the OWF, this paper develops tight polyhedral relaxations of the original MINLP, derives novel valid inequalities (or cuts) using duality theory, and implements novel optimization-based bound tightening and cut generation procedures. The efficacy of each new method is rigorously evaluated by measuring empirical improvements in OWF primal and dual bounds over 45 literature instances. The evaluation suggests that our relaxation improvements, model strengthening techniques, and a thoughtfully selected polyhedral relaxation partitioning scheme can substantially improve OWF primal and dual bounds, especially when compared with similar relaxation-based techniques that do not leverage these new methods. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. Funding: This work was supported by the U.S. Department of Energy (DOE) Advanced Grid Modeling project, Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability. Incorporation of the PolyhedralRelaxations Julia package was supported by Los Alamos National Laboratory’s Directed Research and Development program under the project Fast, Linear Programming-Based Algorithms with Solution Quality Guarantees for Nonlinear Optimal Control Problems [Grant 20220006ER]. All work at Los Alamos National Laboratory was conducted under the auspices of the National Nuclear Security Administration of the U.S. DOE, Contract No. 89233218CNA000001. This work was also authored in part by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. DOE, Contract No. DE-AC36-08GO28308. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0233 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0233 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

This archive is distributed in association with the INFORMS Journal on Computing under a BSD license as part of the Multi-Infrastructure Control and Optimization Toolkit (MICOT) project at Los Alamos National Laboratory, LA-CC-13-108. The software and data in this repository comprise a snapshot of similar artifacts used in the production of the article Polyhedral Relaxations for Optimal Pump Scheduling of Potable Water Distribution Networks by B. Tasseff, R. Bent, C. Coffrin, C. Barrows, D. Sigler, J. Stickel, A. S. Zamzam, Y. Liu, and P. Van Hentenryck. This snapshot is based on the specific inputs and software found in the data and src directories, respectively. Snapshots of unprocessed experimental outputs are provided in the subdirectories of results. Finally, various precompilation, execution, batch cluster submission, and analytical postprocessing utilities are provided in the scripts directory.

This article in MRS Bulletin and the framework set out in the introductory article articulate a scenario of renewable electrons and electrification of end

As large electricity consumers, water distribution system (WDS) pumping stations have the potential to become meaningful participants in demand response (DR) programs. The authors propose an optimization framework for assessing the DR capacity of a WDS and identifying the optimal bidding strategy for maximizing WDS revenue in the DR spot market. The proposed mixed integer linear programming (MILP) model overcomes computational constraints of previous DR optimization models by adopting a preprocessing procedure to minimize the number of binary variables and implementing a convex relaxation technique to linearize the hydraulic equations. The proposed MILP model also explicitly accounts for varying levels of risk tolerance of WDS operators by varying the recovery period over which pumping returns to business-as-usual operation. The optimization framework is implemented on a skeletonized 48-node WDS model that includes 7 pumps, 6 tanks, and 39 pipes. Using a simulated DR event and water consumption profile, the authors derive the optimal DR supply curves (i.e., compensation price versus load curtailment quantity) and revenue potential of the WDS under six scenarios for DR participation.

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result, research communities have proposed a plethora of candidate indicators and metrics for resilience, most of which remain immature at different scales and segments within the energy system. Given the complexity of resilience analyses and mitigation strategies, there is limited value in attempting to identify a single resilience metric, as no one metric can quantify resilience or its associated value for all stakeholders. Instead, a necessary focus of the research community should lie in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing that implementing, testing, and validating resilience metrics are challenges that need to be addressed, the National Renewable Energy Laboratory (NREL) dedicated staff and time to researching how to effectively integrate resilience considerations into energy sector models and tools, as part of the Laboratory Directed Research and Development (LDRD) program. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes - including consequences and/or changes in investment or operational decisions due to avoidedmore » consequences - based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. « less

Recent increase in the number of natural disasters resulting in widespread, long-duration, and costly outages have brought the energy system resilience to the forefront. As system owners and operators seek to improve the system resilience, they often find it challenging to assess the costs and benefits of resilience investments. Costs are fairly well understood and measured, but benefits are less so. There are many resilience metrics that attempt to measure resilience benefit, but the existing methods for calculating metrics and incorporating value of resilience in energy decisions are not easily executed. To address this, we developed a method for modeling the value of resilience that is flexible and scalable across multiple types of models. This article describes a framework for incorporating duration-dependent customer damage functions (CDFs) into grid- and campus-scale planning and operations models. In two case studies, we consider how the duration-dependent value of resilience influences the investment and operation decisions. We find that in both cases, knowledge of the value of lost load provides opportunities to reduce the lifecycle cost of energy through adjusted investment or operational decisions. The primary contribution of this research is to integrate the duration-dependent value of resilience in energy decisions. This study will be useful to grid operators interested in reducing the value of customer losses during grid outages, as well as campus or site owners evaluating resilience investments.

The electric power generation mix of ISO New England (ISO-NE) is fundamentally changing. Nuclear, coal, and oil generation facilities are retiring while natural gas, solar, and wind generation are being adopted to replace them. Variable renewable energy (VREs) such as solar and wind present multiple operational challenges that require new and innovative changes to how the electricity grid is managed and controlled. Within the context of a New England case study, this paper looks at ways in which the water supply systems (water and wastewater treatment), and water dependent electricity generating resources (hydro, and thermal power plants) can be operated flexibly to help balance energy in an evolving grid. The study's methodology employs the novel but now well published Electric Power Enterprise Control System (EPECS) simulator to study the electric power systems operation, and the System-Level Generic Model (SGEM) to study the associated water consumption and withdrawals. This work studies six potential 2040 scenarios for the energy-water nexus of the ISO-NE system. It presents a holistic analysis that quantifies power system imbalances, normal operating reserves, energy market production costs, and water withdrawal and consumption figures. For scenarios with a high penetration of VREs, the study shows great potential of water resources to enhance grid flexibility through the provision of load-following, ramping, and regulation reserves by water resources. The work also provides significant insights on how to jointly control the water and energy supply systems to aid in their synergistic integration.

The evolving nature of electricity production, transmission and consumption necessitates an update to the IEEE's Reliability Test System, which was last modernized in 1996. The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Additional updates include common RTS transmission modifications in published literature, definitions for reserve product requirements and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section presents example results from a production cost modeling simulation on the updated RTS system data.